System for applying an artificial intelligence engine in real-time to affect course corrections and influence outcomes

ABSTRACT

A system for applying an artificial intelligence engine to affect course corrections and influence outcomes of a meeting may include a network interconnecting a facilitator device, a teammate participant device, and a computing system operating a recurrent neural network. The facilitator device may receive parameters of a meeting including one or more of a meeting start time, a meeting location, a meeting duration, a meeting topic, and a list of teammate participant names. The teammate participant device may be a binary meeting score indicating if the meeting was either productive or not productive. The computing system may correlate the meeting score with the meeting parameters to create parameter scores. As scores are stored in the memory of the computing system, over time, the recurrent neural network may transform the parameter scores into parameter suggestions which may be conveyed to a meeting facilitator in real time.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation application tracing priority toco-pending U.S. application Ser. No. 17/717,312 filed on Apr. 11, 2022,the entirety of which is herein expressly incorporated by reference.

TECHNICAL FIELD AND BACKGROUND OF INVENTION

The present invention relates generally to the field of electronicdevice assisted meetings and, more particularly, to improving meetingquality by utilizing real-time feedback from meeting participantsthrough an artificial intelligence engine which transforms the feedbackinto meeting parameter suggestions via a recurrent neural network (RNN).

Meetings are often plagued by inefficiencies, inattentiveness, and otherissues such that some meetings are productive and other meetings arenon-productive. Determining why some meetings are productive and othersare not productive is challenging. Often a meeting may prove to benon-productive for no apparent reason. Anecdotal evidence as to reasonswhy a meeting might be unproductive can be misleading. Accordingly,there exists a need in the art for a system of improving meetingproductivity.

BRIEF SUMMARY

It is therefore an object of the present invention to provide a systemfor improving meeting outcome and quality that may be utilized in realtime by a meeting facilitator.

It is a further object of the present invention to provide a system thatmay be easily implemented so as not to be overly burdensome to meetingparticipants or meeting facilitator(s).

It is a further object of the present invention to provide a system thatthat may be integrated into existing meeting platforms, for instance,Microsoft Teams, Zoom, GoToMeeting, Skype, WebEx, Google Meet, and otherremote meeting platforms.

These and other objects and advantages of the invention are achieved byproviding a system for applying an artificial intelligence engine inreal-time to affect course corrections and influence outcomes of ameeting. The system may include a facilitator device accessible by ahuman facilitator for facilitating the meeting among teammateparticipants. The facilitator device may include a memory device havingcomputer-readable program code. The facilitator device may include acommunication device and a processing device operatively coupled to thememory device and to the communication device. The processing device maybe configured to execute the computer-readable code to receive from thehuman facilitator a plurality of parameters of the meeting including oneor more of a meeting start time, a meeting location, a meeting duration,a meeting topic, and a list of teammate participant names. Theprocessing device may be configured to convey a plurality of futuremeeting parameter suggestions to the human facilitator and to facilitatethe meeting among the human facilitator and teammate participants.

According to one aspect, the system may also include a teammateparticipant device accessible by the teammate participants. The teammateparticipant device may include a memory device having computer-readableprogram code, a communication device, and a processing deviceoperatively coupled to the memory device and to the communicationdevice. The processing device may be configured to execute thecomputer-readable code to communicate with the facilitator device tofacilitate the meeting among the human facilitator and teammateparticipants and receive from the teammate participants a binary meetingscore indicating that the meeting was either productive or notproductive.

According to another aspect, the system may also include a networkinterconnecting the teammate participant device, the facilitator device,and a computing system operating the artificial intelligence engine inthe form of a recurrent neural network (RNN). The computing system mayinclude a memory device having computer-readable program code, acommunication device, and a processing device operatively coupled to thememory device and the communication device. The processing device may beconfigured to execute the computer-readable code to receive from thefacilitator device the plurality of parameters of the meeting, receivefrom the teammate participant device the binary meeting score, correlatethe binary meeting score to each of the plurality of parameters of themeeting to create parameter scores, store the parameter scores in thememory device, receive from the facilitator device an additionalplurality of parameters of a new meeting, receive from the teammateparticipant device a binary additional meeting score, correlate thebinary additional meeting score with the additional plurality ofparameters of the new meeting to create additional parameter scores,store the additional parameter scores in the memory device, and feed theparameter scores and additional parameter scores from the memory deviceinto the RNN to transform the parameter scores and additional parameterscores into the plurality of future meeting parameter suggestions.

According to another embodiment of the invention, the RNN may beconfigured to transform the parameter scores and additional parameterscores into the plurality of future meeting parameter suggestions at apredetermined confidence interval.

According to another embodiment of the invention, the predeterminedconfidence interval may be 95 percent using a t-test.

According to another embodiment of the invention, the predeterminedconfidence interval is 99 percent using a t-test.

According to another embodiment of the invention, the human facilitatorand the teammate participants are not located in a same room.

According to another embodiment of the invention, the meeting is avirtual meeting and wherein the communication device includes a camera,a microphone, and a display device.

According to another embodiment of the invention, the binary meetingscore is selectable by the teammate participants via a binary togglebutton.

According to another embodiment of the invention, a system for applyingan artificial intelligence engine in real-time to affect coursecorrections and influence outcomes of a meeting may include afacilitator device accessible by a human facilitator for facilitatingthe meeting among teammate participants having a processing deviceconfigured to receive meeting parameters and to convey future meetingparameter suggestions. The system may also include a teammateparticipant device accessible by a teammate participant and having aprocessing device configured to receive from the teammate participant abinary meeting score indicating that the meeting was either productiveor not productive. The system may also include a network interconnectingthe teammate participant device, the facilitator device, and a computingsystem operating the artificial intelligence engine in the form of arecurrent neural network (RNN). The computing system may include aprocessing device operatively coupled to a memory device. The processingdevice may be configured to execute the computer-readable code to:receive the meeting parameters from the facilitator device, receive thebinary meeting score from the teammate participant device, correlate thebinary meeting score to each of meeting parameters to create parameterscores, store the parameter scores in the memory device, and feed theparameter scores from the memory device into the RNN to transform theparameter scores into the future meeting parameter suggestions.

According to another embodiment of the invention, the RNN may beconfigured to transform the parameter scores into the future meetingparameter suggestions at a predetermined confidence interval.

According to another embodiment of the invention, the predeterminedconfidence interval may be 95 percent using a t-test.

According to another embodiment of the invention, the predeterminedconfidence interval is 99 percent using a t-test.

According to another embodiment of the invention, the human facilitatorand the teammate participants are not located in a same room.

According to another embodiment of the invention, the meeting may be avirtual meeting and the communication device includes a camera, amicrophone, and a display device.

According to another embodiment, the binary meeting score may beselectable by the teammate participants via a binary toggle button.

According to another embodiment of the invention, the meeting parametersmay include one or more of a meeting start time, a meeting location, ameeting duration, a meeting topic, and a list of teammate participantnames.

According to another embodiment of the invention, the facilitator devicemay further include a communication device for communicating withcomputing system and the teammate participant device via the network anda memory device having a computer-readable program code. The processingdevice may be operably coupled to the memory device to the communicationdevice and may be configured to execute the computer-readable code.

According to another embodiment of the invention, the teammateparticipant device may further include a communication device forcommunicating with computing system and the facilitator device via thenetwork and a memory device having a computer-readable program code. Theprocessing device may be operably coupled to the memory device to thecommunication device and is configured to execute the computer-readablecode.

According to another embodiment of the invention, a system for applyingan artificial intelligence engine in real-time to affect coursecorrections and influence outcomes of a meeting, the system may includea network interconnecting a teammate participant device, a facilitatordevice, and a computing system operating the artificial intelligenceengine in the form of a recurrent neural network (RNN). The computingsystem may include a processing device operatively coupled to a memorydevice. The processing device may be configured to execute thecomputer-readable code to receive a plurality of meeting parameters fromthe facilitator device, receive a binary meeting score from the teammateparticipant device, correlate the binary meeting score to each ofmeeting parameters to create parameter scores, store the parameterscores in the memory device, and feed the parameter scores from thememory device into the RNN to transform the parameter scores into futuremeeting parameter suggestions.

According to another embodiment of the invention, the RNN may beconfigured to transform the parameter scores into the future meetingparameter suggestions at a confidence interval is 95 percent using at-test.

According to another embodiment of the invention, the plurality ofmeeting parameters includes one or more of: a meeting start time, ameeting location, a meeting duration, a meeting topic, and a list ofteammate participant names.

The features, functions, and advantages that have been discussed may beachieved independently in various embodiments of the present inventionor may be combined in yet other embodiments, further details of whichcan be seen with reference to the following description and drawings.

BRIEF DESCRIPTION OF THE DRAWING FIGURES

Having thus described embodiments of the invention in general terms,reference will now be made to the accompanying drawings, wherein:

FIG. 1 illustrates an facilitator system, and environment thereof,according to at least one embodiment;

FIG. 2A is a diagram of a feedforward network, according to at least oneembodiment, utilized in machine learning;

FIG. 2B is a diagram of a convolution neural network, according to atleast one embodiment, utilized in machine learning;

FIG. 2C is a diagram of a portion of the convolution neural network ofFIG. 2B, according to at least one embodiment, illustrating assignedweights at connections or neurons;

FIG. 3 is a diagram representing an exemplary weighted sum computationin a node in an artificial neural network;

FIG. 4 is a diagram of a Recurrent Neural Network RNN, according to atleast one embodiment, utilized in machine learning;

FIG. 5 is a schematic logic diagram of an artificial intelligenceprogram including a front-end and a back-end algorithm;

FIG. 6 is a flow chart representing a method, according to at least oneembodiment, of model development and deployment by machine learning;

FIG. 7 illustrates a system and environment thereof according to oneembodiment of the present invention; and

FIG. 8 is a flow chart representing an embodiment of the system of thepresent invention utilizing the RNN to make suggestions for meetingparameters.

DETAILED DESCRIPTION OF EMBODIMENTS OF THE INVENTION

Embodiments of the present invention will now be described more fullyhereinafter with reference to the accompanying drawings, in which some,but not all, embodiments of the invention are shown. Indeed, theinvention may be embodied in many different forms and should not beconstrued as limited to the embodiments set forth herein; rather, theseembodiments are provided so that this disclosure will satisfy applicablelegal requirements. Like numbers refer to like elements throughout.Unless described or implied as exclusive alternatives, featuresthroughout the drawings and descriptions should be taken as cumulative,such that features expressly associated with some particular embodimentscan be combined with other embodiments. Unless defined otherwise,technical and scientific terms used herein have the same meaning ascommonly understood to one of ordinary skill in the art to which thepresently disclosed subject matter pertains.

The exemplary embodiments are provided so that this disclosure will beboth thorough and complete, and will fully convey the scope of theinvention and enable one of ordinary skill in the art to make, use, andpractice the invention.

The terms “coupled,” “fixed,” “attached to,” “communicatively coupledto,” “operatively coupled to,” and the like refer to both (i) directconnecting, coupling, fixing, attaching, communicatively coupling; and(ii) indirect connecting coupling, fixing, attaching, communicativelycoupling via one or more intermediate components or features, unlessotherwise specified herein. “Communicatively coupled to” and“operatively coupled to” can refer to physically and/or electricallyrelated components.

Embodiments of the present invention described herein, with reference toflowchart illustrations and/or block diagrams of methods or apparatuses(the term “apparatus” includes systems and computer program products),will be understood such that each block of the flowchart illustrationsand/or block diagrams, and combinations of blocks in the flowchartillustrations and/or block diagrams, can be implemented by computerprogram instructions. These computer program instructions may beprovided to a processor of a general purpose computer, special purposecomputer, or other programmable data processing apparatus to produce aparticular machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create mechanisms for implementing the functions/actsspecified in the flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in acomputer-readable memory that can direct a computer or otherprogrammable data processing apparatus to function in a particularmanner, such that the instructions stored in the computer readablememory produce an article of manufacture including instructions, whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer orother programmable data processing apparatus to cause a series ofoperational steps to be performed on the computer or other programmableapparatus to produce a computer implemented process such that theinstructions, which execute on the computer or other programmableapparatus, provide steps for implementing the functions/acts specifiedin the flowchart and/or block diagram block or blocks. Alternatively,computer program implemented steps or acts may be combined with operatoror human implemented steps or acts in order to carry out an embodimentof the invention.

While certain exemplary embodiments have been described and shown in theaccompanying drawings, it is to be understood that such embodiments aremerely illustrative of, and not restrictive on, the broad invention, andthat this invention not be limited to the specific constructions andarrangements shown and described, since various other changes,combinations, omissions, modifications and substitutions, in addition tothose set forth in the above paragraphs, are possible. Those skilled inthe art will appreciate that various adaptations, modifications, andcombinations of the herein described embodiments can be configuredwithout departing from the scope and spirit of the invention. Therefore,it is to be understood that, within the scope of the included claims,the invention may be practiced other than as specifically describedherein.

FIG. 1 illustrates a system 100 and environment thereof, according to atleast one embodiment, by which a teammate 110 benefits through use ofservices of an facilitator system 200. The teammate 110 accessesservices by use of one or more teammate devices, illustrated in separateexamples as a computing device 104 and a mobile device 106, which maybe, as non-limiting examples, a smart phone, a portable digitalassistant (PDA), a pager, a mobile television, a gaming device, a laptopcomputer, a camera, a video recorder, an audio/video player, radio, aGPS device, or any combination of the aforementioned, or other portabledevice with processing and communication capabilities. In theillustrated example, the mobile device 106 is illustrated in FIG. 1 ashaving exemplary elements, the below descriptions of which apply as wellto the computing device 104, which can be, as non-limiting examples, adesktop computer, a laptop computer, or other teammate-accessiblecomputing device.

Furthermore, the teammate device, referring to either or both of thecomputing device 104 and the mobile device 106, may be or include aworkstation, a server, or any other suitable device, including a set ofservers, a cloud-based application or system, or any other suitablesystem, adapted to execute, for example any suitable operating system,including Linux, UNIX, Windows, macOS, iOS, Android and any other knownoperating system used on personal computers, central computing systems,phones, and other devices.

The teammate 110 can be an individual, a group, or any entity inpossession of or having access to the teammate device, referring toeither or both of the mobile device 104 and computing device 106, whichmay be personal or public items. Although the teammate 110 may be singlyrepresented in some drawings, at least in some embodiments according tothese descriptions the teammate 110 is one of many such that a market orcommunity of teammates, consumers, customers, business entities,government entities, clubs, and groups of any size are all within thescope of these descriptions.

The teammate device, as illustrated with reference to the mobile device106, includes components such as, at least one of each of a processingdevice 120, and a memory device 122 for processing use, such as randomaccess memory (RAM), and read-only memory (ROM). The illustrated mobiledevice 106 further includes a storage device 124 including at least oneof a non-transitory storage medium, such as a microdrive, for long-term,intermediate-term, and short-term storage of computer-readableinstructions 126 for execution by the processing device 120. Forexample, the instructions 126 can include instructions for an operatingsystem and various applications or programs 130, of which theapplication 132 is represented as a particular example. The storagedevice 124 can store various other data items 134, which can include, asnon-limiting examples, cached data, teammate files such as those forpictures, audio and/or video recordings, files downloaded or receivedfrom other devices, and other data items preferred by the teammate orrequired or related to any or all of the applications or programs 130.

The memory device 122 is operatively coupled to the processing device120. As used herein, memory includes any computer readable medium tostore data, code, or other information. The memory device 122 mayinclude volatile memory, such as volatile Random Access Memory (RAM)including a cache area for the temporary storage of data. The memorydevice 122 may also include non-volatile memory, which can be embeddedand/or may be removable. The non-volatile memory can additionally oralternatively include an electrically erasable programmable read-onlymemory (EEPROM), flash memory or the like.

The memory device 122 and storage device 124 can store any of a numberof applications which comprise computer-executable instructions and codeexecuted by the processing device 120 to implement the functions of themobile device 106 described herein. For example, the memory device 122may include such applications as a conventional web browser applicationand/or a mobile P2P payment system client application. Theseapplications also typically provide a graphical teammate interface (GUI)on the display 140 that allows the teammate 110 to communicate with themobile device 106, and, for example a mobile banking system, and/orother devices or systems. In one embodiment, when the teammate 110decides to participate in a meeting, the teammate 110 downloads orotherwise obtains the meeting system client application from a mobilemeeting system, for example facilitator system 200, or from a distinctapplication server. In other embodiments, the teammate 110 interactswith a mobile meeting system via a web browser application in additionto, or instead of, the mobile P2P payment system client application.

The processing device 120, and other processors described herein,generally include circuitry for implementing communication and/or logicfunctions of the mobile device 106. For example, the processing device120 may include a digital signal processor, a microprocessor, andvarious analog to digital converters, digital to analog converters,and/or other support circuits. Control and signal processing functionsof the mobile device 106 are allocated between these devices accordingto their respective capabilities. The processing device 120 thus mayalso include the functionality to encode and interleave messages anddata prior to modulation and transmission. The processing device 120 canadditionally include an internal data modem. Further, the processingdevice 120 may include functionality to operate one or more softwareprograms, which may be stored in the memory device 122, or in thestorage device 124. For example, the processing device 120 may becapable of operating a connectivity program, such as a web browserapplication. The web browser application may then allow the mobiledevice 106 to transmit and receive web content, such as, for example,location-based content and/or other web page content, according to aWireless Application Protocol (WAP), Hypertext Transfer Protocol (HTTP),and/or the like.

The memory device 122 and storage device 124 can each also store any ofa number of pieces of information, and data, used by the teammate deviceand the applications and devices that facilitate functions of theteammate device, or are in communication with the teammate device, toimplement the functions described herein and others not expresslydescribed. For example, the storage device may include such data asteammate authentication information, etc.

The processing device 120, in various examples, can operatively performcalculations, can process instructions for execution, and can manipulateinformation. The processing device 120 can execute machine-executableinstructions stored in the storage device 124 and/or memory device 122to thereby perform methods and functions as described or implied herein,for example by one or more corresponding flow charts expressly providedor implied as would be understood by one of ordinary skill in the art towhich the subject matters of these descriptions pertain. The processingdevice 120 can be or can include, as non-limiting examples, a centralprocessing unit (CPU), a microprocessor, a graphics processing unit(GPU), a microcontroller, an application-specific integrated circuit(ASIC), a programmable logic device (PLD), a digital signal processor(DSP), a field programmable gate array (FPGA), a state machine, acontroller, gated or transistor logic, discrete physical hardwarecomponents, and combinations thereof. In some embodiments, particularportions or steps of methods and functions described herein areperformed in whole or in part by way of the processing device 120, whilein other embodiments methods and functions described herein includecloud-based computing in whole or in part such that the processingdevice 120 facilitates local operations including, as non-limitingexamples, communication, data transfer, and teammate inputs and outputssuch as receiving commands from and providing displays to the teammate.

The mobile device 106, as illustrated, includes an input and outputsystem 136, referring to, including, or operatively coupled with,teammate input devices and teammate output devices, which areoperatively coupled to the processing device 120. The teammate outputdevices include a display 140 (e.g., a liquid crystal display or thelike), which can be, as a non-limiting example, a touch screen of themobile device 106, which serves both as an output device, by providinggraphical and text indicia and presentations for viewing by one or moreteammate 110, and as an input device, by providing virtual buttons,selectable options, a virtual keyboard, and other indicia that, whentouched, control the mobile device 106 by teammate action. The teammateoutput devices include a speaker 144 or other audio device. The teammateinput devices, which allow the mobile device 106 to receive data andactions such as button manipulations and touches from a teammate such asthe teammate 110, may include any of a number of devices allowing themobile device 106 to receive data from a teammate, such as a keypad,keyboard, touch-screen, touchpad, microphone 142, mouse, joystick, otherpointer device, button, soft key, and/or other input device(s). Theteammate interface may also include a camera 146, such as a digitalcamera.

Further non-limiting examples include, one or more of each, any, and allof a wireless or wired keyboard, a mouse, a touchpad, a button, aswitch, a light, an LED, a buzzer, a bell, a printer and/or otherteammate input devices and output devices for use by or communicationwith the teammate 110 in accessing, using, and controlling, in whole orin part, the teammate device, referring to either or both of thecomputing device 104 and a mobile device 106. Inputs by one or moreteammate 110 can thus be made via voice, text or graphical indiciaselections. For example, such inputs in some examples correspond toteammate-side actions and communications seeking services and productsof the facilitator system 200, and at least some outputs in suchexamples correspond to data representing facilitator-side actions andcommunications in two-way communications between a teammate 110 and anfacilitator system 200.

The mobile device 106 may also include a positioning device 108, whichcan be for example a global positioning system device (GPS) configuredto be used by a positioning system to determine a location of the mobiledevice 106. For example, the positioning system device 108 may include aGPS transceiver. In some embodiments, the positioning system device 108includes an antenna, transmitter, and receiver. For example, in oneembodiment, triangulation of cellular signals may be used to identifythe approximate location of the mobile device 106. In other embodiments,the positioning device 108 includes a proximity sensor or transmitter,such as an RFID tag, that can sense or be sensed by devices known to belocated proximate a merchant or other location to determine that theconsumer mobile device 106 is located proximate these known devices.

In the illustrated example, a system intraconnect 138, connects, forexample electrically, the various described, illustrated, and impliedcomponents of the mobile device 106. The intraconnect 138, in variousnon-limiting examples, can include or represent, a system bus, ahigh-speed interface connecting the processing device 120 to the memorydevice 122, individual electrical connections among the components, andelectrical conductive traces on a motherboard common to some or all ofthe above-described components of the teammate device. As discussedherein, the system intraconnect 138 may operatively couple variouscomponents with one another, or in other words, electrically connectsthose components, either directly or indirectly—by way of intermediatecomponent(s)—with one another.

The teammate device, referring to either or both of the computing device104 and the mobile device 106, with particular reference to the mobiledevice 106 for illustration purposes, includes a communication interface150, by which the mobile device 106 communicates and conductstransactions with other devices and systems. The communication interface150 may include digital signal processing circuitry and may providetwo-way communications and data exchanges, for example wirelessly viawireless communication device 152, and for an additional or alternativeexample, via wired or docked communication by mechanical electricallyconductive connector 154. Communications may be conducted via variousmodes or protocols, of which GSM voice calls, SMS, EMS, MMS messaging,TDMA, CDMA, PDC, WCDMA, CDMA2000, and GPRS, are all non-limiting andnon-exclusive examples. Thus, communications can be conducted, forexample, via the wireless communication device 152, which can be orinclude a radio-frequency transceiver, a Bluetooth device, Wi-Fi device,a Near-field communication device, and other transceivers. In addition,GPS (Global Positioning System) may be included for navigation andlocation-related data exchanges, ingoing and/or outgoing. Communicationsmay also or alternatively be conducted via the connector 154 for wiredconnections such by USB, Ethernet, and other physically connected modesof data transfer.

The processing device 120 is configured to use the communicationinterface 150 as, for example, a network interface to communicate withone or more other devices on a network. In this regard, thecommunication interface 150 utilizes the wireless communication device152 as an antenna operatively coupled to a transmitter and a receiver(together a “transceiver”) included with the communication interface150. The processing device 120 is configured to provide signals to andreceive signals from the transmitter and receiver, respectively. Thesignals may include signaling information in accordance with the airinterface standard of the applicable cellular system of a wirelesstelephone network. In this regard, the mobile device 106 may beconfigured to operate with one or more air interface standards,communication protocols, modulation types, and access types. By way ofillustration, the mobile device 106 may be configured to operate inaccordance with any of a number of first, second, third, fourth,fifth-generation communication protocols and/or the like. For example,the mobile device 106 may be configured to operate in accordance withsecond-generation (2G) wireless communication protocols IS-136 (timedivision multiple access (TDMA)), GSM (global system for mobilecommunication), and/or IS-95 (code division multiple access (CDMA)), orwith third-generation (3G) wireless communication protocols, such asUniversal Mobile Telecommunications System (UMTS), CDMA2000, widebandCDMA (WCDMA) and/or time division-synchronous CDMA (TD-SCDMA), withfourth-generation (4G) wireless communication protocols such asLong-Term Evolution (LTE), fifth-generation (5G) wireless communicationprotocols, Bluetooth Low Energy (BLE) communication protocols such asBluetooth 5.0, ultra-wideband (UWB) communication protocols, and/or thelike. The mobile device 106 may also be configured to operate inaccordance with non-cellular communication mechanisms, such as via awireless local area network (WLAN) or other communication/data networks.

The communication interface 150 may also include a payment networkinterface. The payment network interface may include software, such asencryption software, and hardware, such as a modem, for communicatinginformation to and/or from one or more devices on a network. Forexample, the mobile device 106 may be configured so that it can be usedas a credit or debit card by, for example, wirelessly communicatingaccount numbers or other authentication information to a terminal of thenetwork. Such communication could be performed via transmission over awireless communication protocol such as the Near-field communicationprotocol.

The mobile device 106 further includes a power source 128, such as abattery, for powering various circuits and other devices that are usedto operate the mobile device 106. Embodiments of the mobile device 106may also include a clock or other timer configured to determine and, insome cases, communicate actual or relative time to the processing device120 or one or more other devices. For further example, the clock mayfacilitate timestamping transmissions, receptions, and other data forsecurity, authentication, logging, polling, data expiry, and forensicpurposes.

System 100 as illustrated diagrammatically represents at least oneexample of a possible implementation, where alternatives, additions, andmodifications are possible for performing some or all of the describedmethods, operations and functions. Although shown separately, in someembodiments, two or more systems, servers, or illustrated components mayutilized. In some implementations, the functions of one or more systems,servers, or illustrated components may be provided by a single system orserver. In some embodiments, the functions of one illustrated system orserver may be provided by multiple systems, servers, or computingdevices, including those physically located at a central facility, thoselogically local, and those located as remote with respect to each other.

The facilitator system 200 can offer many types of meeting services andproducts to one or more teammates 110. Use of “service(s)” or“product(s)” thus relates to either or both in these descriptions. Withregard, for example, to online information and meeting services,“service” and “product” are sometimes termed interchangeably. Innon-limiting examples, meeting services might include online virtualmeetings via platforms such as Microsoft Teams, Zoom, GoToMeeting,Skype, WebEx, Google Meet, and other remote meeting platforms.

To provide access to, or information regarding, some or all the servicesand products of the facilitator system 200, automated assistance may beprovided by the facilitator system 200. For example, automated access toteammate accounts and replies to inquiries may be provided byfacilitator-side automated voice, text, and graphical displaycommunications and interactions. In at least some examples, multiplehuman facilitators 210, can be employed, utilized, authorized orreferred by the facilitator system 200.

Human facilitators 210 may utilize facilitator devices 212 to serveteammates in their interactions to communicate and take action. Thefacilitator devices 212 can be, as non-limiting examples, computingdevices, kiosks, terminals, smart devices such as phones, and devicesand tools at customer service counters and windows at POS locations. Inat least one example, the diagrammatic representation of the componentsof the teammate device 106 in FIG. 1 applies as well to one or both ofthe computing device 104 and the facilitator devices 212.

Facilitator devices 212 individually or collectively include inputdevices and output devices, including, as non-limiting examples, a touchscreen, which serves both as an output device by providing graphical andtext indicia and presentations for viewing by one or more facilitator210, and as an input device by providing virtual buttons, selectableoptions, a virtual keyboard, and other indicia that, when touched oractivated, control or prompt the facilitator device 212 by action of theattendant facilitator 210. Further non-limiting examples include, one ormore of each, any, and all of a keyboard, a mouse, a touchpad, ajoystick, a button, a switch, a light, an LED, a microphone serving asinput device for example for voice input by a human facilitator 210, aspeaker serving as an output device, a camera serving as an inputdevice, a buzzer, a bell, a printer and/or other teammate input devicesand output devices for use by or communication with a human facilitator210 in accessing, using, and controlling, in whole or in part, thefacilitator device 212.

Inputs by one or more human facilitators 210 can thus be made via voice,text or graphical indicia selections. For example, some inputs receivedby a facilitator device 212 in some examples correspond to, control, orprompt facilitator-side actions and communications offering services andproducts of the facilitator system 200, information thereof, or accessthereto. At least some outputs by a facilitator device 212 in someexamples correspond to, or are prompted by, teammate-side actions andcommunications in two-way communications between a teammate 110 and anfacilitator-side human facilitator 210.

From a teammate perspective experience, an interaction in some exampleswithin the scope of these descriptions begins with direct or firstaccess to one or more human facilitators 210 in person, by phone, oronline for example via a chat session or website function or feature. Inother examples, a teammate is first assisted by a virtual facilitator214 of the facilitator system 200, which may satisfy teammate requestsor prompts by voice, text, or online functions, and may refer teammatesto one or more human facilitators 210 once preliminary determinations orconditions are made or met.

A computing system 206 of the facilitator system 200 may includecomponents such as, at least one of each of a processing device 220, anda memory device 222 for processing use, such as random access memory(RAM), and read-only memory (ROM). The illustrated computing system 206further includes a storage device 224 including at least onenon-transitory storage medium, such as a microdrive, for long-term,intermediate-term, and short-term storage of computer-readableinstructions 226 for execution by the processing device 220. Forexample, the instructions 226 can include instructions for an operatingsystem and various applications or programs 230, of which theapplication 232 is represented as a particular example. The storagedevice 224 can store various other data 234, which can include, asnon-limiting examples, cached data, and files such as those for teammateaccounts, teammate profiles, account balances, and transactionhistories, files downloaded or received from other devices, and otherdata items preferred by the teammate or required or related to any orall of the applications or programs 230.

The computing system 206, in the illustrated example, includes aninput/output system 236, referring to, including, or operatively coupledwith input devices and output devices such as, in a non-limitingexample, facilitator devices 212, which have both input and outputcapabilities.

In the illustrated example, a system intraconnect 238 electricallyconnects the various above-described components of the computing system206. In some cases, the intraconnect 238 operatively couples componentsto one another, which indicates that the components may be directly orindirectly connected, such as by way of one or more intermediatecomponents. The intraconnect 238, in various non-limiting examples, caninclude or represent, a system bus, a high-speed interface connectingthe processing device 220 to the memory device 222, individualelectrical connections among the components, and electrical conductivetraces on a motherboard common to some or all of the above-describedcomponents of the teammate device.

The computing system 206, in the illustrated example, includes acommunication interface 250, by which the computing system 206communicates and conducts transactions with other devices and systems.The communication interface 250 may include digital signal processingcircuitry and may provide two-way communications and data exchanges, forexample wirelessly via wireless device 252, and for an additional oralternative example, via wired or docked communication by mechanicalelectrically conductive connector 254. Communications may be conductedvia various modes or protocols, of which GSM voice calls, SMS, EMS, MMSmessaging, TDMA, CDMA, PDC, WCDMA, CDMA2000, and GPRS, are allnon-limiting and non-exclusive examples. Thus, communications can beconducted, for example, via the wireless device 252, which can be orinclude a radio-frequency transceiver, a Bluetooth device, Wi-Fi device,Near-field communication device, and other transceivers. In addition,GPS (Global Positioning System) may be included for navigation andlocation-related data exchanges, ingoing and/or outgoing. Communicationsmay also or alternatively be conducted via the connector 254 for wiredconnections such as by USB, Ethernet, and other physically connectedmodes of data transfer.

The processing device 220, in various examples, can operatively performcalculations, can process instructions for execution, and can manipulateinformation. The processing device 220 can execute machine-executableinstructions stored in the storage device 224 and/or memory device 222to thereby perform methods and functions as described or implied herein,for example by one or more corresponding flow charts expressly providedor implied as would be understood by one of ordinary skill in the art towhich the subjects matters of these descriptions pertain. The processingdevice 220 can be or can include, as non-limiting examples, a centralprocessing unit (CPU), a microprocessor, a graphics processing unit(GPU), a microcontroller, an application-specific integrated circuit(ASIC), a programmable logic device (PLD), a digital signal processor(DSP), a field programmable gate array (FPGA), a state machine, acontroller, gated or transistor logic, discrete physical hardwarecomponents, and combinations thereof.

Furthermore, the computing device 206, may be or include a workstation,a server, or any other suitable device, including a set of servers, acloud-based application or system, or any other suitable system, adaptedto execute, for example any suitable operating system, including Linux,UNIX, Windows, macOS, iOS, Android, and any known other operating systemused on personal computer, central computing systems, phones, and otherdevices.

The teammate devices, referring to either or both of the mobile device104 and computing device 106, the facilitator devices 212, and thefacilitator computing system 206, which may be one or any numbercentrally located or distributed, are in communication through one ormore networks, referenced as network 258 in FIG. 1 .

Network 258 provides wireless or wired communications among thecomponents of the system 100 and the environment thereof, includingother devices local or remote to those illustrated, such as additionalmobile devices, servers, and other devices communicatively coupled tonetwork 258, including those not illustrated in FIG. 1 . The network 258is singly depicted for illustrative convenience, but may include morethan one network without departing from the scope of these descriptions.In some embodiments, the network 258 may be or provide one or morecloud-based services or operations. The network 258 may be or include anfacilitator or secured network, or may be implemented, at least in part,through one or more connections to the Internet. A portion of thenetwork 258 may be a virtual private network (VPN) or an Intranet. Thenetwork 258 can include wired and wireless links, including, asnon-limiting examples, 802.11a/b/g/n/ac, 802.20, WiMax, LTE, and/or anyother wireless link. The network 258 may include any internal orexternal network, networks, sub-network, and combinations of suchoperable to implement communications between various computingcomponents within and beyond the illustrated environment 100. Thenetwork 258 may communicate, for example, Internet Protocol (IP)packets, Frame Relay frames, Asynchronous Transfer Mode (ATM) cells,voice, video, data, and other suitable information between networkaddresses. The network 258 may also include one or more local areanetworks (LANs), radio access networks (RANs), metropolitan areanetworks (MANs), wide area networks (WANs), all or a portion of theinternet and/or any other communication system or systems at one or morelocations.

Two external systems 202 and 204 are expressly illustrated in FIG. 1 ,representing any number and variety of data storage, data processing,and machine learning processors. In at least one example, the externalsystems 202 and 204 represent an artificial intelligence engine, such asa recurrent neural network, utilized by the facilitator system 200 infacilitating meetings among teammates 110. In another example, theexternal systems 202 and 204 represent third party systems suchconfigured to interact with the teammate devices 106 during meetings andalso configured to interact with the facilitator system 200 in meetings.

In certain embodiments, one or more of the systems such as the teammatedevice 106, the facilitator system 200, and/or the external systems 202and 204 are, include, or utilize virtual resources. In some cases, suchvirtual resources are considered cloud resources or virtual machines.Such virtual resources may be available for shared use among multipledistinct resource consumers and in certain implementations, virtualresources do not necessarily correspond to one or more specific piecesof hardware, but rather to a collection of pieces of hardwareoperatively coupled within a cloud computing configuration so that theresources may be shared as needed.

As used herein, an artificial intelligence system, artificialintelligence algorithm, artificial intelligence module, program, and thelike, generally refer to computer implemented programs that are suitableto simulate intelligent behavior (i.e., intelligent human behavior)and/or computer systems and associated programs suitable to performtasks that typically require a human to perform, such as tasks requiringvisual perception, speech recognition, decision-making, translation, andthe like. An artificial intelligence system may include, for example, atleast one of a series of associated if-then logic statements, astatistical model suitable to map raw sensory data into symboliccategories and the like, or a machine learning program. A machinelearning program, machine learning algorithm, or machine learningmodule, as used herein, is generally a type of artificial intelligenceincluding one or more algorithms that can learn and/or adjust parametersbased on input data provided to the algorithm. In some instances,machine learning programs, algorithms, and modules are used at least inpart in implementing artificial intelligence (AI) functions, systems,and methods.

Artificial Intelligence and/or machine learning programs may beassociated with or conducted by one or more processors, memory devices,and/or storage devices of a computing system or device. It should beappreciated that the AI algorithm or program may be incorporated withinthe existing system architecture or be configured as a standalonemodular component, controller, or the like communicatively coupled tothe system. An AI program and/or machine learning program may generallybe configured to perform methods and functions as described or impliedherein, for example by one or more corresponding flow charts expresslyprovided or implied as would be understood by one of ordinary skill inthe art to which the subjects matters of these descriptions pertain.

A machine learning program may be configured to implement storedprocessing, such as decision tree learning, association rule learning,artificial neural networks, recurrent artificial neural networks, longshort term memory networks, inductive logic programming, support vectormachines, clustering, Bayesian networks, reinforcement learning,representation learning, similarity and metric learning, sparsedictionary learning, genetic algorithms, k-nearest neighbor (KNN), andthe like. In some embodiments, the machine learning algorithm mayinclude one or more image recognition algorithms suitable to determineone or more categories to which an input, such as data communicated froma visual sensor or a file in JPEG, PNG or other format, representing animage or portion thereof, belongs. Additionally or alternatively, themachine learning algorithm may include one or more regression algorithmsconfigured to output a numerical value given an input. Further, themachine learning may include one or more pattern recognition algorithms,e.g., a module, subroutine or the like capable of translating text orstring characters and/or a speech recognition module or subroutine. Invarious embodiments, the machine learning module may include a machinelearning acceleration logic, e.g., a fixed function matrixmultiplication logic, in order to implement the stored processes and/oroptimize the machine learning logic training and interface.

One type of algorithm suitable for use in machine learning modules asdescribed herein is an artificial neural network or neural network,taking inspiration from biological neural networks. An artificial neuralnetwork can, in a sense, learn to perform tasks by processing examples,without being programmed with any task-specific rules. A neural networkgenerally includes connected units, neurons, or nodes (e.g., connectedby synapses) and may allow for the machine learning program to improveperformance. A neural network may define a network of functions, whichhave a graphical relationship. As an example, a feedforward network maybe utilized, e.g., an acyclic graph with nodes arranged in layers.

A feedforward network (see, e.g., feedforward network 260 referenced inFIG. 2A) may include a topography with a hidden layer 264 between aninput layer 262 and an output layer 266. The input layer 262, havingnodes commonly referenced in FIG. 2A as input nodes 272 for convenience,communicates input data, variables, matrices, or the like to the hiddenlayer 264, having nodes 274. The hidden layer 264 generates arepresentation and/or transformation of the input data into a form thatis suitable for generating output data. Adjacent layers of thetopography are connected at the edges of the nodes of the respectivelayers, but nodes within a layer typically are not separated by an edge.In at least one embodiment of such a feedforward network, data iscommunicated to the nodes 272 of the input layer, which thencommunicates the data to the hidden layer 264. The hidden layer 264 maybe configured to determine the state of the nodes in the respectivelayers and assign weight coefficients or parameters of the nodes basedon the edges separating each of the layers, e.g., an activation functionimplemented between the input data communicated from the input layer 262and the output data communicated to the nodes 276 of the output layer266. It should be appreciated that the form of the output from theneural network may generally depend on the type of model represented bythe algorithm. Although the feedforward network 260 of FIG. 2A expresslyincludes a single hidden layer 264, other embodiments of feedforwardnetworks within the scope of the descriptions can include any number ofhidden layers. The hidden layers are intermediate the input and outputlayers and are generally where all or most of the computation is done.

Neural networks may perform a supervised learning process where knowninputs and known outputs are utilized to categorize, classify, orpredict a quality of a future input. However, additional or alternativeembodiments of the machine learning program may be trained utilizingunsupervised or semi-supervised training, where none of the outputs orsome of the outputs are unknown, respectively. Typically, a machinelearning algorithm is trained (e.g., utilizing a training data set)prior to modeling the problem with which the algorithm is associated.Supervised training of the neural network may include choosing a networktopology suitable for the problem being modeled by the network andproviding a set of training data representative of the problem.Generally, the machine learning algorithm may adjust the weightcoefficients until any error in the output data generated by thealgorithm is less than a predetermined, acceptable level. For instance,the training process may include comparing the generated output producedby the network in response to the training data with a desired orcorrect output. An associated error amount may then be determined forthe generated output data, such as for each output data point generatedin the output layer. The associated error amount may be communicatedback through the system as an error signal, where the weightcoefficients assigned in the hidden layer are adjusted based on theerror signal. For instance, the associated error amount (e.g., a valuebetween −1 and 1) may be used to modify the previous coefficient, e.g.,a propagated value. The machine learning algorithm may be consideredsufficiently trained when the associated error amount for the outputdata is less than the predetermined, acceptable level (e.g., each datapoint within the output layer includes an error amount less than thepredetermined, acceptable level). Thus, the parameters determined fromthe training process can be utilized with new input data to categorize,classify, and/or predict other values based on the new input data.

An additional or alternative type of neural network suitable for use inthe machine learning program and/or module is a Convolutional NeuralNetwork (CNN). A CNN is a type of feedforward neural network that may beutilized to model data associated with input data having a grid-liketopology. In some embodiments, at least one layer of a CNN may include asparsely connected layer, in which each output of a first hidden layerdoes not interact with each input of the next hidden layer. For example,the output of the convolution in the first hidden layer may be an inputof the next hidden layer, rather than a respective state of each node ofthe first layer. CNNs are typically trained for pattern recognition,such as speech processing, language processing, and visual processing.As such, CNNs may be particularly useful for implementing optical andpattern recognition programs required from the machine learning program.A CNN includes an input layer, a hidden layer, and an output layer,typical of feedforward networks, but the nodes of a CNN input layer aregenerally organized into a set of categories via feature detectors andbased on the receptive fields of the sensor, retina, input layer, etc.Each filter may then output data from its respective nodes tocorresponding nodes of a subsequent layer of the network. A CNN may beconfigured to apply the convolution mathematical operation to therespective nodes of each filter and communicate the same to thecorresponding node of the next subsequent layer. As an example, theinput to the convolution layer may be a multidimensional array of data.The convolution layer, or hidden layer, may be a multidimensional arrayof parameters determined while training the model.

An exemplary convolutional neural network CNN is depicted and referencedas 280 in FIG. 2B. As in the basic feedforward network 260 of FIG. 2A,the illustrated example of FIG. 2B has an input layer 282 and an outputlayer 286. However where a single hidden layer 264 is represented inFIG. 2A, multiple consecutive hidden layers 284A, 284B, and 284C arerepresented in FIG. 2B. The edge neurons represented by white-filledarrows highlight that hidden layer nodes can be connected locally, suchthat not all nodes of succeeding layers are connected by neurons. FIG.2C, representing a portion of the convolutional neural network 280 ofFIG. 2B, specifically portions of the input layer 282 and the firsthidden layer 284A, illustrates that connections can be weighted. In theillustrated example, labels W1 and W2 refer to respective assignedweights for the referenced connections. Two hidden nodes 283 and 285share the same set of weights W1 and W2 when connecting to two localpatches.

Weight defines the impact a node in any given layer has on computationsby a connected node in the next layer. FIG. 3 represents a particularnode 300 in a hidden layer. The node 300 is connected to several nodesin the previous layer representing inputs to the node 300. The inputnodes 301, 302, 303 and 304 are each assigned a respective weight W01,W02, W03, and W04 in the computation at the node 300, which in thisexample is a weighted sum.

An additional or alternative type of feedforward neural network suitablefor use in the machine learning program and/or module is a RecurrentNeural Network (RNN). An RNN may allow for analysis of sequences ofinputs rather than only considering the current input data set. RNNstypically include feedback loops/connections between layers of thetopography, thus allowing parameter data to be communicated betweendifferent parts of the neural network. RNNs typically have anarchitecture including cycles, where past values of a parameterinfluence the current calculation of the parameter, e.g., at least aportion of the output data from the RNN may be used as feedback/input incalculating subsequent output data. In some embodiments, the machinelearning module may include an RNN configured for language processing,e.g., an RNN configured to perform statistical language modeling topredict the next word in a string based on the previous words. TheRNN(s) of the machine learning program may include a feedback systemsuitable to provide the connection(s) between subsequent and previouslayers of the network.

An example for a Recurrent Neural Network RNN is referenced as 400 inFIG. 4 . As in the basic feedforward network 260 of FIG. 2A, theillustrated example of FIG. 4 has an input layer 410 (with nodes 412)and an output layer 440 (with nodes 442). However, where a single hiddenlayer 264 is represented in FIG. 2A, multiple consecutive hidden layers420 and 430 are represented in FIG. 4 (with nodes 422 and nodes 432,respectively). As shown, the RNN 400 includes a feedback connector 404configured to communicate parameter data from at least one node 432 fromthe second hidden layer 430 to at least one node 422 of the first hiddenlayer 420. It should be appreciated that two or more and up to all ofthe nodes of a subsequent layer may provide or communicate a parameteror other data to a previous layer of the RNN network 400. Moreover andin some embodiments, the RNN 400 may include multiple feedbackconnectors 404 (e.g., connectors 404 suitable to communicatively couplepairs of nodes and/or connector systems 404 configured to providecommunication between three or more nodes). Additionally oralternatively, the feedback connector 404 may communicatively couple twoor more nodes having at least one hidden layer between them, i.e., nodesof nonsequential layers of the RNN 400.

In an additional or alternative embodiment, the machine learning programmay include one or more support vector machines. A support vectormachine may be configured to determine a category to which input databelongs. For example, the machine learning program may be configured todefine a margin using a combination of two or more of the inputvariables and/or data points as support vectors to maximize thedetermined margin. Such a margin may generally correspond to a distancebetween the closest vectors that are classified differently. The machinelearning program may be configured to utilize a plurality of supportvector machines to perform a single classification. For example, themachine learning program may determine the category to which input databelongs using a first support vector determined from first and seconddata points/variables, and the machine learning program mayindependently categorize the input data using a second support vectordetermined from third and fourth data points/variables. The supportvector machine(s) may be trained similarly to the training of neuralnetworks, e.g., by providing a known input vector (including values forthe input variables) and a known output classification. The supportvector machine is trained by selecting the support vectors and/or aportion of the input vectors that maximize the determined margin.

As depicted, and in some embodiments, the machine learning program mayinclude a neural network topography having more than one hidden layer.In such embodiments, one or more of the hidden layers may have adifferent number of nodes and/or the connections defined between layers.In some embodiments, each hidden layer may be configured to perform adifferent function. As an example, a first layer of the neural networkmay be configured to reduce a dimensionality of the input data, and asecond layer of the neural network may be configured to performstatistical programs on the data communicated from the first layer. Invarious embodiments, each node of the previous layer of the network maybe connected to an associated node of the subsequent layer (denselayers). Generally, the neural network(s) of the machine learningprogram may include a relatively large number of layers, e.g., three ormore layers, and are referred to as deep neural networks. For example,the node of each hidden layer of a neural network may be associated withan activation function utilized by the machine learning program togenerate an output received by a corresponding node in the subsequentlayer. The last hidden layer of the neural network communicates a dataset (e.g., the result of data processed within the respective layer) tothe output layer. Deep neural networks may require more computationaltime and power to train, but the additional hidden layers providemultistep pattern recognition capability and/or reduced output errorrelative to simple or shallow machine learning architectures (e.g.,including only one or two hidden layers).

Referring now to FIG. 5 and some embodiments, an AI program 502 mayinclude a front-end algorithm 504 and a back-end algorithm 506. Theartificial intelligence program 502 may be implemented on an AIprocessor 520, such as the processing device 120, the processing device220, and/or a dedicated processing device. The instructions associatedwith the front-end algorithm 504 and the back-end algorithm 506 may bestored in an associated memory device and/or storage device of thesystem (e.g., memory device 124 and/or memory device 224)communicatively coupled to the AI processor 520, as shown. Additionallyor alternatively, the system may include one or more memory devicesand/or storage devices (represented by memory 524 in FIG. 5 ) forprocessing use and/or including one or more instructions necessary foroperation of the AI program 502. In some embodiments, the AI program 502may include a deep neural network (e.g., a front-end network 504configured to perform pre-processing, such as feature recognition, and aback-end network 506 configured to perform an operation on the data setcommunicated directly or indirectly to the back-end network 506). Forinstance, the front-end program 506 can include at least one CNN 508communicatively coupled to send output data to the back-end network 506.

Additionally or alternatively, the front-end program 504 can include oneor more AI algorithms 510, 512 (e.g., statistical models or machinelearning programs such as decision tree learning, associate rulelearning, recurrent artificial neural networks, support vector machines,and the like). In various embodiments, the front-end program 504 may beconfigured to include built in training and inference logic or suitablesoftware to train the neural network prior to use (e.g., machinelearning logic including, but not limited to, image recognition, mappingand localization, autonomous navigation, speech synthesis, documentimaging, or language translation). For example, a CNN 508 and/or AIalgorithm 510 may be used for image recognition, input categorization,and/or support vector training. In some embodiments and within thefront-end program 504, an output from an AI algorithm 510 may becommunicated to a CNN 508 or 509, which processes the data beforecommunicating an output from the CNN 508, 509 and/or the front-endprogram 504 to the back-end program 506. In various embodiments, theback-end network 506 may be configured to implement input and/or modelclassification, speech recognition, translation, and the like. Forinstance, the back-end network 506 may include one or more CNNs (e.g.,CNN 514) or dense networks (e.g., dense networks 516), as describedherein.

For instance and in some embodiments of the AI program 502, the programmay be configured to perform unsupervised learning, in which the machinelearning program performs the training process using unlabeled data,e.g., without known output data with which to compare. During suchunsupervised learning, the neural network may be configured to generategroupings of the input data and/or determine how individual input datapoints are related to the complete input data set (e.g., via thefront-end program 504). For example, unsupervised training may be usedto configure a neural network to generate a self-organizing map, reducethe dimensionally of the input data set, and/or to performoutlier/anomaly determinations to identify data points in the data setthat falls outside the normal pattern of the data. In some embodiments,the AI program 502 may be trained using a semi-supervised learningprocess in which some but not all of the output data is known, e.g., amix of labeled and unlabeled data having the same distribution.

In some embodiments, the AI program 502 may be accelerated via a machinelearning framework 520 (e.g., hardware). The machine learning frameworkmay include an index of basic operations, subroutines, and the like(primitives) typically implemented by AI and/or machine learningalgorithms. Thus, the AI program 502 may be configured to utilize theprimitives of the framework 520 to perform some or all of thecalculations required by the AI program 502. Primitives suitable forinclusion in the machine learning framework 520 include operationsassociated with training a convolutional neural network (e.g., pools),tensor convolutions, activation functions, basic algebraic subroutinesand programs (e.g., matrix operations, vector operations), numericalmethod subroutines and programs, and the like.

It should be appreciated that the machine learning program may includevariations, adaptations, and alternatives suitable to perform theoperations necessary for the system, and the present disclosure isequally applicable to such suitably configured machine learning and/orartificial intelligence programs, modules, etc. For instance, themachine learning program may include one or more long short-term memory(LSTM) RNNs, convolutional deep belief networks, deep belief networksDBNs, and the like. DBNs, for instance, may be utilized to pre-train theweighted characteristics and/or parameters using an unsupervisedlearning process. Further, the machine learning module may include oneor more other machine learning tools (e.g., Logistic Regression (LR),Naïve-Bayes, Random Forest (RF), matrix factorization, and supportvector machines) in addition to, or as an alternative to, one or moreneural networks, as described herein.

FIG. 6 is a flow chart representing a method 600, according to at leastone embodiment, of model development and deployment by machine learning.The method 600 represents at least one example of a machine learningworkflow in which steps are implemented in a machine learning project.

In step 602, a teammate authorizes, requests, manages, or initiates themachine-learning workflow. This may represent a teammate such as humanfacilitator, or customer, requesting machine-learning assistance or AIfunctionality to simulate intelligent behavior (such as a virtualfacilitator) or other machine-assisted or computerized tasks that may,for example, entail visual perception, speech recognition,decision-making, translation, forecasting, predictive modelling, and/orsuggestions as non-limiting examples. In a first iteration from theteammate perspective, step 602 can represent a starting point. However,with regard to continuing or improving an ongoing machine learningworkflow, step 602 can represent an opportunity for further teammateinput or oversight via a feedback loop.

In step 604, data is received, collected, accessed, or otherwiseacquired and entered as can be termed data ingestion. In step 606 thedata ingested in step 604 is pre-processed, for example, by cleaning,and/or transformation such as into a format that the followingcomponents can digest. The incoming data may be versioned to connect adata snapshot with the particularly resulting trained model. As newlytrained models are tied to a set of versioned data, preprocessing stepsare tied to the developed model. If new data is subsequently collectedand entered, a new model will be generated. If the preprocessing step606 is updated with newly ingested data, an updated model will begenerated. Step 606 can include data validation, which focuses onconfirming that the statistics of the ingested data are as expected,such as that data values are within expected numerical ranges, that datasets are within any expected or required categories, and that datacomply with any needed distributions such as within those categories.Step 606 can proceed to step 608 to automatically alert the initiatingteammate, other human or virtual facilitators, and/or other systems, ifany anomalies are detected in the data, thereby pausing or terminatingthe process flow until corrective action is taken.

In step 610, training test data such as a target variable value isinserted into an iterative training and testing loop. In step 612, modeltraining, a core step of the machine learning work flow, is implemented.A model architecture is trained in the iterative training and testingloop. For example, features in the training test data are used to trainthe model based on weights and iterative calculations in which thetarget variable may be incorrectly predicted in an early iteration asdetermined by comparison in step 614, where the model is tested.Subsequent iterations of the model training, in step 612, may beconducted with updated weights in the calculations.

When compliance and/or success in the model testing in step 614 isachieved, process flow proceeds to step 616, where model deployment istriggered. The model may be utilized in AI functions and programming,for example to simulate intelligent behavior, to performmachine-assisted or computerized tasks, of which visual perception,speech recognition, decision-making, translation, forecasting,predictive modelling, and/or automated suggestion generation serve asnon-limiting examples.

Referring now to FIG. 7 and FIG. 8 , according to one embodiment of thepresent invention, a system for applying an artificial intelligenceengine in real-time to affect course corrections and to influenceoutcomes of a meeting may employee the system 100. As shown in FIG. 7 ,the system 100 may include a plurality of human teammate participants110 and a single human facilitator 210. According to the embodiment, thehuman facilitator 210 may be tasked with facilitating a meeting as shownin FIG. 8 . The meeting may be conducted virtually over the network 258.The meeting may use a remote platform similar to that operated byMicrosoft Teams, Zoom, GoToMeeting, Skype, WebEx, Google Meet, and otherremote meeting platforms.

When facilitating a meeting, the human facilitator 210 utilizes afacilitator device 206 which includes memory device 222 havingcomputer-readable code 226. The facilitator device 206 also hascommunication device 250 and processing device 220.

When participating in a meeting, each human teammate 110 participantutilizes a teammate participant device 106. Each teammate participantdevice 106 includes memory device 122 having computer-readable code 126.Each teammate participant device 106 also has communication device 150and processing device 120. The participant device 106 communicates withthe facilitator device 206 via network 248.

Also in communication with the teammate participant device 106 and thefacilitator device 206 via network 258 is the computing system 702 whichoperates the artificial intelligence engine in the form of the recurrentneural network (RNN) 812, 814 as show in FIG. 8 . See also FIG. 4 . Thecomputing system 702 includes memory device 722 having computer-readablecode 726. The computing system 702 also has communication device 750 andprocessing device 720. The computing system 702 communicates with thefacilitator device 206 and the teammate participant device 106 vianetwork 258.

When a human facilitator 210 desires to conduct a meeting among humanteammate participants 110, the human facilitator 210 must firstdetermine a listing of meeting parameters. See FIG. 8 . The parametersmay include one or more of a meeting start time, a meeting location, ameeting duration, a meeting topic, and a list of teammate participantnames. The human facilitator 210 enters these parameters into thefacilitator device 206 as show at numeral 802 in FIG. 8 . Thefacilitator and the teammate participants then engage in the meeting804. At the conclusion of the meeting, the teammate participants thenenter a binary meeting score 806. The binary meeting score indicateseither that the meeting was productive or that it was not productive.The binary meeting score is then transmitted 808 to the computing system702 which then correlates the binary meeting score with each of themeeting parameters which were entered by the facilitator 210 prior tothe meeting. As part of this process, each parameter is scored.

This process is then repeated 810 such that successive meetings andrespective meeting parameters set by the facilitator are scored and thedata stored in the memory of the computing system 702. Over time, thesescores are fed into the RNN 812. The RNN operates as disclosed above andin FIG. 4 . Over time, the RNN is trained to learn information about themeeting parameters. For instance, the RNN might determine that meetingson a certain topic are best held in the morning, or that meetingsincluding certain of the teammates are better held in the afternoon, orthat certain meetings are best held by teammates in a certainlylocation. That is, based on the binary scoring that has been correlatedto each parameter, the RNN learns optimal parameters for differentclusters of parameters. Accordingly, over time, the RNN will reach apredetermined confidence interval, which might be 95% or greater, atwhich the RNN will be able to make meeting parameter suggestions 814.These meeting parameter suggestions will be conveyed to the humanfacilitator 816. These meeting parameter suggestions may aid the humanfacilitator as the human facilitator is planning future meetings 802.

For instance, once the RNN has reached the predetermined confidenceinterval, and once the human facilitator begins to enter new meetingparameters 802, the RNN will make suggestions 816 in order to improvemeeting productivity. For instance and by way of one non-limitingexample, if the human facilitator desires to conduct a meeting at 8 AMFriday morning on the topic of planning and involving teammates John,Jane, and Cory, based on what the RNN has learned from previousparameter scores, the RNN may have learned that a meeting with John,Jane, and Cory on the topic of planning may be more productive if heldThursday at 1 PM. Accordingly, the suggestion 816 to move the meeting toThursday at 1 PM would be made to the human facilitator 210. Similarly,if the meeting facilitator desires to conduct a meeting at 3 PM onWednesday on the topic of strategy, involving Jim, Jack, and George andlasting 2 hrs, based on what the RNN has learned from previous parameterscores, the RNN may have learned that a meeting with Jim, Jack, andGeorge on the topic of strategy may be more productive if, rather thanlasting 2 hrs, it could be shortened to 45 minutes. Or, should alsoinclude Barbara. Or should be conducted over lunch. Or should beconducted outside.

Accordingly, over time, the RNN is able to be trained on meetingparameters and how these parameters interact to form productive andnon-productive meetings. Utilizing RNN, the computing system is able totransform mere binary parameter scores into real time feedback providedto human facilitators in order to affect course corrects and to improvemeeting outcomes.

Particular embodiments and features have been described with referenceto the drawings. It is to be understood that these descriptions are notlimited to any single embodiment or any particular set of features.Similar embodiments and features may arise or modifications andadditions may be made without departing from the scope of thesedescriptions and the spirit of the appended claims.

What is claimed is:
 1. An apparatus comprising: a server having aprocessor and a memory, operably connected to the processor, on which isstored an artificial intelligence engine; a facilitator device connectedto the server via a network and configured to receive a plurality ofmeeting parameters from a human facilitator; and a plurality of teammatedevices connected to the server via the network and configured toreceive from a human teammate a binary meeting score; wherein theartificial intelligence engine is configured to receive from thefacilitator device the plurality of meeting parameters and to receivefrom the plurality of teammate devices the binary meeting scores;wherein the artificial intelligence engine operates a recurrent neuralnetwork (RNN) that correlates the plurality of meeting parameters withthe binary meeting scores to produce correlated meeting data; andwherein, after a predetermined confidence interval has been achieved,the RNN transforms the correlated meeting data into future meetingparameter suggestions.
 2. The apparatus of claim 1 wherein thepredetermined confidence interval is 95 percent using a t-test.
 3. Theapparatus of claim 1 wherein in the predetermined confidence interval is99 percent using a t-test.
 4. The apparatus of claim 1 wherein thefacilitator device is configured to host a virtual meeting.
 5. Theapparatus of claim 1 wherein the binary meeting score indicates that themeeting was either productive or not productive.
 6. The apparatus ofclaim 5 wherein the binary meeting score is selectable via a binarytoggle button located on a display of the teammate device.
 7. Theapparatus of claim 1 wherein the plurality of meeting parameters includeone or more of: a meeting start time, a meeting location, a meetingduration, a meeting topic, and a list of teammate participant names. 8.The apparatus of claim 1 wherein each of the teammate devices of theplurality of teammate devices comprises: a memory device havingcomputer-readable program code; a communication device; a processingdevice operatively coupled to the memory device and to the communicationdevice, wherein the processing device is configured to execute thecomputer-readable code to: communicate with the facilitator device andthe server to facilitate the meeting, and receive the binary meetingscore from the human teammate.
 9. The apparatus of claim 1 wherein thefacilitator device comprises: a memory device having computer-readableprogram code; a communication device; a processing device operativelycoupled to the memory device and to the communication device, whereinthe processing device is configured to execute the computer-readablecode to: receive the plurality of meeting parameters from the humanfacilitator, convey the plurality of future meeting parametersuggestions to the human facilitator; and facilitate the meeting. 10.The apparatus of claim 1 wherein the RNN comprises an input layer, anoutput layer, and a plurality of consecutive hidden layers.
 11. Anapparatus comprising: an artificial intelligence engine having arecurrent neural network (RNN) loaded on a server and configured to:receive from a meeting facilitator a plurality of meeting parametersfrom prior to the initiation of a meeting; receive from a teammateparticipant a plurality of meeting scores following the conclusion ameeting; correlate the plurality of meeting parameters and the pluralityof meeting scores into a correlated meeting data set; store thecorrelated meeting data; transform the correlated meeting data intofuture meeting parameter suggestions after a predetermined confidenceinterval has been achieved.
 12. The apparatus of claim 11 wherein theartificial intelligence engine is loaded on a server having a processorand a memory, operably connected to the processor.
 13. The apparatus ofclaim 12 further comprising a facilitator device connected to the servervia a network and configured to receive the plurality of meetingparameters from the human facilitator.
 14. The apparatus of claim 13further comprising a plurality of teammate devices connected to theserver via the network and configured to receive from the human teammatethe binary meeting score.
 15. The apparatus of claim 14 wherein thepredetermined confidence interval is 95 percent using a t-test.
 16. Theapparatus of claim 14 wherein in the predetermined confidence intervalis 99 percent using a t-test.
 17. The apparatus of claim 14 wherein thefacilitator device is configured to host a virtual meeting.
 18. Theapparatus of claim 11 wherein the binary meeting score indicates thatthe meeting was either productive or not productive.
 19. The apparatusof claim 18 wherein the binary meeting score is selectable via a binarytoggle button located on a display of the teammate device.
 20. Theapparatus of claim 19 wherein the plurality of meeting parametersinclude one or more of: a meeting start time, a meeting location, ameeting duration, a meeting topic, and a list of teammate participantnames.